METHOD OF LOAD FORECASTING VIA KNOWLEDGE DISTILLATION, AND AN APPARATUS FOR THE SAME

    公开(公告)号:US20230102489A1

    公开(公告)日:2023-03-30

    申请号:US17902626

    申请日:2022-09-02

    Abstract: A server may obtain teacher artificial intelligence (AI) models from source base stations; obtain target traffic data from a target base station; obtain an integrated teacher prediction based on the target traffic data by integrating teacher prediction results of the teacher AI models based on teacher importance weights; obtain a student AI model that is trained to converge a student loss on the target traffic data; update the teacher importance weights to converge a teacher loss between a student prediction of the student AI model on the target traffic data, and the integrated teacher prediction of the teacher AI models on the target traffic data; update the student AI model based on the updated teacher importance weights being applied to the teacher prediction results of the teacher AI models; and predict a communication traffic load of the target base station using the updated student AI model.

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